Instructions to use diffusers-modular/MiniMax-H3-LightVAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers-modular/MiniMax-H3-LightVAE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("diffusers-modular/MiniMax-H3-LightVAE", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
File size: 2,119 Bytes
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"_class_name": "AutoencoderKLMiniMaxH3LightVAE",
"_diffusers_version": "0.40.0.dev0",
"block_out_channels": [
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],
"clip_length": 17,
"decoder_attention_head_dim": 64,
"decoder_ffn_mult": 4,
"decoder_norm_eps": 1e-05,
"decoder_num_attention_heads": 32,
"decoder_num_layers": 12,
"decoder_num_register_tokens": 4,
"decoder_rope_dim_ratio": 0.75,
"decoder_rope_theta": 100.0,
"in_channels": 3,
"latent_channels": 24,
"latents_mean": [
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"latents_std": [
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"layers_per_block": 2,
"norm_eps": 1e-06,
"norm_num_groups": 32,
"out_channels": 3,
"spatial_downsample_factors": [
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],
"spatial_padding_mode": "reflect",
"temporal_downsample_factors": [
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],
"token_drop": 3,
"auto_map": {
"AutoModel": "modeling_minimax_h3_lightvae.AutoencoderKLMiniMaxH3LightVAE"
}
} |